Most prominenty, we have several new rating predictors, all of them variants of asymmetric factor models (AFMs).
The new item recommender MostPopularByItemAttributes generalizes an idea presented by the organizers of the Million Song Database Challenge. We now have 27 different rating predictors and 18 different item recommenders in MyMediaLite.

Existing recommender have received significant improvements:

WRMF: support out-of-the-box multi-core learning (#233)

BiasedMatrixFactorization: 'naive' parallelization

(Sigmoid)SVDPlusPlus, SigmoidItemAsymmetricFactorModel: take the
ratings that have to be predicted into account (transductive learning)

evaluation improvements: different modes for selecting candidate items now also work for cross-validation; new evaluation measure: mean reciprocal rank (MRR); move item recommender evaluation measures into their own classes; move online evaluation and cross-validation for both items and ratings into their own classes, respectively

Item prediction tool: allow specification of the set of items to consider for evaluation, the items in the training set (default), a given set (via --relevant-items=FILE), the ones in the test set(--test-items), or only those items in both the training and the test set --overlap-items); save time by evaluation on a random subset of the users (--num-test-users=N).

The library (and the command-line tools) now also offer an online evaluation protocol that uses incremental updates.

Important announcement: This may be the last version to support Mono 2.6. If you use MyMediaLite and rely on running it on Mono 2.6, please tell us so that we can figure out how you will be able to run future versions of MML.

This is mostly a bugfix release:
- Fixed broken save/load mechanism for two recommenders.
- Fixed the Doxygen documentation, which was configured wrongly.
See the Changes file for details and further improvements.

This is mostly a bugfix release:
- Fixed broken save/load mechanism for two recommenders.
- Fixed the Doxygen documentation, which was configured wrongly.
See the Changes file for details and further improvements.

Version 1.0 features major changes in the API and in the way the ratings/user feedback is stored internally. This makes it feasible to load big datasets like the ones from KDD Cup 2011 into main memory. We also ship extra code and a command-line program to handle the KDD Cup data, and some example Python scripts.
See the Changes file for details and further improvements.

Version 0.11 features a simple Gtk#-based demo of some of the library's functionality. Feedback and improvements are very welcome, as we are not GUI specialists (yet).

There have been the following improvements:
SlopeOne is now much faster and consumes less memory.
We fixed some bugs in the online update functionality of the matrix factorization recommenders.
We now support the MovieLens 1M/10M ratings format.

The command-line tools now use reflection to automagically find all relevant recommenders. This means you do not have to modify the command-line tools any more to use your newly implemented recommenders!
The kNN-based methods are now faster and consume less memory because they take data sparsity better into account.
A method for the diversification of result sets has been added to the experimental section of MyMediaLite.
Some namespaces and types have been renamed to have nicer, more intuitive names.

The most notable changes are support for reading data from SQL databases (and other data sources supporting the IDataReader interface), the addition of the Slope One rating prediction engine, and initial support for crossvalidation.

Most notably, we now have the API documentation in the packages and on the website. Another programmer-visible change is that our namespaces now have CamelCase instead of lower_case names. This allows MyMediaLite to be called from IronRuby.